Ai-Driven Hyper-Personalization In Omnichannel Marketing: A Case Study In Fashion Retail

Hyper-personalization in omnichannel marketing is crucial for creating customer value, increasing engagement, and improving conversion rates. However, many existing frameworks struggle to deliver coherent, personalized, context-aware recommendations that present the right products at the right time, through the right channel, within an orchestrated journey that maximizes customer-perceived value and brand value. Building on a prior conceptual framework for AI-driven hyper-personalization in omnichannel marketing, this paper presents an empirical evolution, instantiated and evaluated on a real fashion retail dataset. The proposed system operationalizes three decision dimensions: a Next Best Offer module (what), combining cascade-filter collaborative signals with complementary-product discovery via product-graph community detection (Louvain), alternative-product ranking via TF-IDF similarity, and a data-driven price sensitivity model; a Next Best Channel module (where) that compares Reinforcement Learning (RL), deep learning (LSTM), and Random Forest approaches for predicting the most effective communication channel; and a Retrieval-Augmented Generation (RAG) module (how) that transforms these predictions into executable marketing actions. The solution was evaluated using precision, recall, accuracy, F1-score, perceived recommendation adequacy, and business-oriented indicators. Results indicate that the architecture enhances recommendation adequacy, aligns with customers’ price profiles, and RF models outperform static heuristics and LSTM in engagement metrics, demonstrating the effectiveness of the AI stack in fashion retail hyper-personalization.